Performance Comparison of T۵ and Marian Machine Translation Models for English to French and German Translation

This paper presents a comparative analysis of two prominent machine translation models, T۵ and Marian, focusing on their performance in translating English text into French and German. The primary objective is to evaluate and contrast the quality of translations generated by these models, employing the METEOR score as a key metric for assessing translation accuracy. The methodology involves loading pre-trained models, defining input texts, generating translations using both T۵ and Marian, and subsequently computing METEOR scores to quantify translation quality. Experimental results indicate that the Marian model exhibits superior performance in English-to-French translation compared to T۵. However, T۵ demonstrates competitive results in translating English into German. Furthermore, the inference time of each model is assessed and reported. The findings underscore that the optimal choice between T۵ and Marian is contingent upon the target language and specific application requirements, highlighting the significance of employing standardized evaluation metrics such as METEOR to enhance machine translation systems.

کلیدواژه ها:

Machine Translation ، T۵ ، Marian ، METEOR Score ، English to French Translation ، English to German Translation